Three-dimensional reconstruction method, equipment, system and device and storage medium

Abstract

The invention provides a three-dimensional reconstruction method, equipment, system and device and a storage medium. The method comprises the following steps: acquiring a coded stripe pattern which is projected to the surface of a measured object and is based on time sequence change; the coding stripe pattern comprises two groups of first sinusoidal grating stripe images with the same periodicity and at least one group of second sinusoidal grating stripe images with the periodicity less than that of the first sinusoidal grating stripe images; obtaining first three-dimensional point cloud data according to the group of first sinusoidal grating stripe images and the at least one group of second sinusoidal grating stripe images; obtaining second three-dimensional point cloud data according to the other group of first sinusoidal grating stripe images and at least one group of second sinusoidal grating stripe images; the first three-dimensional point cloud data and the second three-dimensional point cloud data are converted to the same pose and then fused, and third three-dimensional point cloud data are obtained; and determining a three-dimensional surface model of the measured object according to the third three-dimensional point cloud data. According to the method, two pieces of three-dimensional point cloud data are reconstructed according to a group of coding stripe patterns, and the three-dimensional reconstruction effect is better.

Description

Technical Field

[0001] This application belongs to the field of three-dimensional reconstruction technology, and in particular relates to three-dimensional reconstruction methods, equipment, systems, devices and storage media. Background Technology

[0002] In current structured light 3D scanning technology, a sinusoidal grating fringe image is projected onto the object being measured by the DLP (Digital Light Processing Projector) optical engine in the 3D scanning equipment, and then the object is reconstructed in 3D.

[0003] Structured light 3D scanning technology has advantages such as non-contact measurement and higher efficiency compared to single-line / multi-line lasers. However, it is easily affected by the surrounding environment and defects on the surface of the object being measured, which in turn affects the measurement accuracy. Summary of the Invention

[0004] This application provides a three-dimensional reconstruction method, device, system, apparatus, and storage medium, which can reduce the problem that structured light three-dimensional scanning is easily affected by the surrounding environment and defects on the surface of the object being measured.

[0005] In a first aspect, embodiments of this application provide a three-dimensional reconstruction method, including:

[0006] Acquire a time-varying coded stripe pattern projected onto the surface of a measured object by a 3D scanning device; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images;

[0007] Based on a set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images, the first three-dimensional point cloud data of the surface of the object under test is obtained;

[0008] Based on another set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images, the second three-dimensional point cloud data of the surface of the object under test is obtained;

[0009] The first three-dimensional point cloud data and the second three-dimensional point cloud data are converted to the same pose and then fused to obtain the third three-dimensional point cloud data of the surface of the object under test.

[0010] Based on the third three-dimensional point cloud data, the three-dimensional surface model of the object under test is determined.

[0011] In one possible implementation of the first aspect, obtaining the first three-dimensional point cloud data of the surface of the object under test based on a set of first sinusoidal grating fringe images and the at least one set of second sinusoidal grating fringe images includes:

[0012] Based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images, determine the phase distribution of each pixel in a set of first sinusoidal grating fringe images;

[0013] Based on the phase distribution of each pixel in a set of the first sinusoidal grating stripe images, the first depth information of the surface of the object under test is obtained;

[0014] Based on the first depth information of the surface of the object being measured, the first three-dimensional point cloud data of the surface of the object being measured is determined.

[0015] In one possible implementation of the first aspect, the group of second sinusoidal grating fringe images with the fewest period numbers among the at least one group of second sinusoidal grating fringe images has a period number of 1; each group of first sinusoidal grating fringe images and second sinusoidal grating fringe images has at least three sinusoidal grating fringe images, and the at least three sinusoidal grating fringe images have the same period number and a fixed phase difference;

[0016] The step of determining the phase distribution of each pixel in a set of first sinusoidal grating fringe images based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images includes:

[0017] The phase of a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images are solved respectively to obtain the phase principal value of a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images at each pixel.

[0018] Align the phase principal values ​​of a set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images at the period edges;

[0019] Taking the period number as the order of small to large, and based on the multiple relationship of the period number between two adjacent second sinusoidal grating stripe images, the phase distribution of the next second sinusoidal grating stripe image at each pixel is determined step by step according to the phase principal value of the previous second sinusoidal grating stripe image at each pixel.

[0020] The phase distribution of each pixel in a set of first sinusoidal grating fringe images is determined based on the phase distribution of each pixel in the second sinusoidal grating fringe image with the largest number of periods in the at least one set of second sinusoidal grating fringe images, and the multiple relationship between the number of periods of the second sinusoidal grating fringe image with the largest number of periods in the at least one set of second sinusoidal grating fringe images and a set of first sinusoidal grating fringe images.

[0021] In one possible implementation of the first aspect, the method further includes:

[0022] The moving speed of the 3D scanning device is determined based on the pose of the first 3D point cloud data and the pose of the second 3D point cloud data.

[0023] Save and / or display in real time the moving speed of the 3D scanning device.

[0024] In one possible implementation of the first aspect, the method further includes:

[0025] When the moving speed of the 3D scanning device exceeds the preset speed, a warning message is issued.

[0026] Secondly, embodiments of this application provide a three-dimensional scanning device, including a projection device, a camera, and a processor;

[0027] The projection device is used to project a time-varying coded stripe pattern onto the surface of the object being measured; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images.

[0028] The camera is used to capture the coded stripe pattern on the surface of the object being measured;

[0029] The processor is configured to obtain first three-dimensional point cloud data of the surface of the object under test based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images.

[0030] Based on another set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images, the second three-dimensional point cloud data of the surface of the object under test is obtained;

[0031] The first and second 3D point cloud data are converted to the same pose and then fused to obtain the third 3D point cloud data of the surface of the object under test; and

[0032] Based on the third three-dimensional point cloud data, the three-dimensional surface model of the object under test is determined.

[0033] In one possible implementation of the second aspect, the projection device is a MEMS structured light module.

[0034] Thirdly, embodiments of this application provide a three-dimensional scanning system, including a three-dimensional scanning device and an electronic device;

[0035] The three-dimensional scanning device is used to project a time-varying coded stripe pattern onto the surface of the object being measured; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images.

[0036] The three-dimensional scanning device is also used to capture the coded stripe pattern on the surface of the object being measured;

[0037] The electronic device is used to obtain first three-dimensional point cloud data of the surface of the object under test based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images.

[0038] Based on another set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images, the second three-dimensional point cloud data of the surface of the object under test is obtained;

[0039] The first and second 3D point cloud data are converted to the same pose and then fused to obtain the third 3D point cloud data of the surface of the object under test; and

[0040] Based on the third three-dimensional point cloud data, the three-dimensional surface model of the object under test is determined.

[0041] Fourthly, embodiments of this application provide a three-dimensional reconstruction apparatus, comprising:

[0042] The acquisition module is used to acquire a time-varying coded stripe pattern projected by a 3D scanning device onto the surface of the object under test; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images;

[0043] The first determining module is used to obtain the first three-dimensional point cloud data of the surface of the object under test based on a set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images.

[0044] The second determining module is used to obtain the second three-dimensional point cloud data of the surface of the object under test based on another set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images.

[0045] The fusion module is used to convert the first three-dimensional point cloud data and the second three-dimensional point cloud data to the same pose and then fuse them to obtain the third three-dimensional point cloud data of the surface of the object under test.

[0046] The third determining module is used to determine the three-dimensional surface model of the object under test based on the third three-dimensional point cloud data.

[0047] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any of the first aspects.

[0048] In a sixth aspect, embodiments of this application provide a computer program product that, when run on a 3D scanning device, causes the 3D scanning device to perform the method described in any one of the first aspects above.

[0049] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0050] The beneficial effects of this application embodiment compared to the prior art are as follows: This application embodiment designs two sets of first sinusoidal grating fringe images with a large number of periods and at least one set of second sinusoidal grating fringe images with a small number of periods in the coded fringe pattern. The at least one set of low-frequency second sinusoidal grating fringe images is reused repeatedly, and two 3D point cloud data are reconstructed with the two sets of first sinusoidal grating fringe images respectively. Then, the point clouds of the two 3D point cloud data are converted to the same pose and fused. Thus, one coded fringe pattern reconstructs two 3D point cloud data, resulting in more point cloud data and better 3D reconstruction effect. It also shortens the scanning time and improves the efficiency of 3D reconstruction. Furthermore, it utilizes the complementary advantages of sinusoidal grating fringe images with large and small periods, allowing the first sinusoidal grating fringe image with a large number of periods to provide detail, and the second sinusoidal grating fringe image with a small number of periods to provide global stability and robustness. This combination helps to reduce errors during 3D reconstruction, reduce the influence of factors such as the surrounding environment and defects on the surface of the measured object, and improve overall accuracy. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic flowchart of a three-dimensional reconstruction method provided in an embodiment of this application;

[0053] Figure 2a This is a sinusoidal grating fringe image with a period number of 1 provided in one embodiment of this application;

[0054] Figure 2b This is a sinusoidal grating fringe image with a period number of 4 provided in one embodiment of this application;

[0055] Figure 2c This is a sinusoidal grating fringe image with a period number of 16 provided in one embodiment of this application;

[0056] Figure 2dThis is a sinusoidal grating fringe image with a period number of 64 provided in one embodiment of this application;

[0057] Figure 3 This is a partial structural schematic diagram of a three-dimensional scanning device provided in an embodiment of this application;

[0058] Figure 4 This is a partial structural schematic diagram of a three-dimensional scanning device provided in another embodiment of this application;

[0059] Figure 5 This is a schematic diagram of the structure of a three-dimensional scanning system provided in an embodiment of this application;

[0060] Figure 6 This is a schematic diagram of the structure of a three-dimensional scanning device provided in an embodiment of this application. Detailed Implementation

[0061] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0062] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0063] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0064] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0065] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0066] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0067] Figure 1 A schematic flowchart of a three-dimensional reconstruction method provided in an embodiment of this application is shown, including the following steps:

[0068] S110: Acquire a time-varying coded stripe pattern projected by a 3D scanning device onto the surface of the object being measured; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images.

[0069] Optionally, the coded stripe pattern is projected onto the surface of the object being measured by the imaging device in the 3D scanning device, and the coded stripe pattern on the surface of the object being measured is captured one by one by the camera in the 3D scanning device. Then, the coded stripe pattern captured one by one by the camera is acquired by the processing device (such as a computer, the processor in the 3D scanning device, or other electronic devices with high data processing capabilities).

[0070] Encoded stripe patterns refer to arranging multiple sets of sinusoidal grating stripe images in chronological order. Each set of sinusoidal grating stripe images includes at least three sinusoidal grating stripe images with a fixed phase difference. Then, the projection device in the 3D scanning equipment projects the multiple sets of sinusoidal grating stripe images one by one onto the surface of the object being measured.

[0071] For example, the coded stripe pattern consists of 15 sinusoidal grating stripe images arranged in chronological order: 3 sinusoidal grating stripe images with a period of 64, 3 sinusoidal grating stripe images with a period of 16, 3 sinusoidal grating stripe images with a period of 64, 3 sinusoidal grating stripe images with a period of 4, and 3 sinusoidal grating stripe images with a period of 1. The two sets of 3 sinusoidal grating stripe images with a period of 64 correspond to two sets of first sinusoidal grating stripe images, while the remaining three sets—3 sinusoidal grating stripe images with a period of 16, 3 sinusoidal grating stripe images with a period of 4, and 3 sinusoidal grating stripe images with a period of 1—correspond to three sets of second sinusoidal grating stripe images. The duration of each of the 15 sinusoidal grating stripe images in a given set of coded stripe patterns is very short, often measured in milliseconds. After one set of coded stripe patterns is projected, the next set is projected, and this process is repeated until the object being measured has been completely scanned.

[0072] A sinusoidal grating fringe image is a light intensity distribution image with sinusoidal waveform characteristics. Figures 2a-2d Images of sinusoidal grating fringes with different periods are shown, in which Figure 2a A sinusoidal grating fringe image with a period of 1 means that the entire area projected by the projection device in the 3D scanning equipment contains only one complete period of sinusoidal grating fringe image. Figure 2b It is a sinusoidal grating fringe image with a period of 4, that is, the entire area projected by the projection device in the 3D scanning device has a sinusoidal grating fringe image with 4 complete periods. Figure 2c It is a sinusoidal grating fringe image with a period of 16, that is, the entire area projected by the projection device in the 3D scanning device has a sinusoidal grating fringe image with 16 complete periods. Figure 2d It is a sinusoidal grating fringe image with a period of 64, that is, the entire area projected by the projection device in the 3D scanning device has a sinusoidal grating fringe image with 64 complete periods.

[0073] When a sinusoidal grating fringe image is projected onto the surface of a measured object, the inconsistent height of the surface causes distortion of the grating image, which is manifested through phase change. A mathematical model can be used to establish a mapping relationship between the phase and the object's surface height. This model considers factors such as the projection device's position, the camera's viewing angle, the geometric characteristics of the sinusoidal grating fringe image, and the object's surface reflectivity. Through phase resolution, the phase value corresponding to each pixel can be calculated. Then, based on this phase value and the pre-established mapping relationship, the height or depth of the measured object's surface corresponding to that pixel can be deduced.

[0074] It is easy to understand that the frequency of a sinusoidal grating fringe image refers to the number of periods of the fringe per unit length. Within the entire projection area of ​​a 3D scanning device, the more periods of the sinusoidal grating fringe image projected, the higher the frequency of the sinusoidal grating fringe image.

[0075] The coded fringe pattern includes two sets of first sinusoidal grating fringe images with the same number of periods, and at least one set of second sinusoidal grating fringe images with fewer periods than the first sinusoidal grating fringe images. It is easy to understand that the more periods the projected sinusoidal grating fringe images have, the higher the frequency and the denser the fringes (e.g., ...). Figure 2d The more sensitive a sine grating is to smaller structural changes, the more and more subtle the height variation information of the measured object's surface can be carried. However, it is also more susceptible to the influence of the surrounding environment and defects on the surface of the measured object, resulting in image noise. Image noise also increases the impact on phase calculation, and the accuracy of phase solution decreases. Therefore, each group of first sinusoidal grating fringe images can carry more height variation information, but at the same time, it introduces errors in phase solution. The fewer the number of periods and the lower the frequency of the projected sinusoidal grating fringe image, the sparser the fringes (e.g., ...). Figure 2a While its ability to capture subtle changes on the surface of an object is relatively weak, resulting in lower accuracy of phase information, it is less susceptible to the influence of the surrounding environment and defects on the surface of the object being measured. The phase change is also more gradual, making the phase unfolding process relatively easy, reducing errors and ambiguities in phase understanding, and improving global stability and robustness. Therefore, although each set of second sinusoidal grating fringe images carries less height variation information, it reduces errors in phase understanding.

[0076] S120: Based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images, obtain the first three-dimensional point cloud data of the surface of the object being measured.

[0077] The term "a set" refers to any one of the two sets of first sinusoidal grating fringe images. The term "at least one set of second sinusoidal grating fringe images" refers to all sets of second sinusoidal grating fringe images.

[0078] As mentioned above, the first sinusoidal grating fringe image can carry more height variation information, but it is also prone to introducing errors in phase resolution. The second sinusoidal grating fringe image carries less height variation information, but it reduces the errors that occur in phase resolution. The two have complementary characteristics. Therefore, by utilizing the advantages of a set of second sinusoidal grating fringe images in phase resolution, we can make up for the disadvantages of a set of first sinusoidal grating fringe images in phase resolution. In addition, the first sinusoidal grating fringe images can carry more height variation information, thus achieving complementary advantages. Based on this, the first three-dimensional point cloud data constructed has high accuracy.

[0079] For example, at least one set of second sinusoidal grating fringe images includes three sets of sinusoidal grating fringe images with period numbers of 16, 4, and 1, while one set of first sinusoidal grating fringe images is a set of sinusoidal grating fringe images with a period number of 64. The advantage of the three sets of sinusoidal grating fringe images with period numbers of 16, 4, and 1 in phase resolution compensates for the disadvantage of the single set of sinusoidal grating fringe images with a period number of 64 in phase resolution. Combined with the advantage of the single set of sinusoidal grating fringe images with a period number of 64 in carrying more height variation information, a complementary advantage is formed, resulting in higher accuracy of the reconstructed 3D point cloud data.

[0080] S130: Based on another set of first sinusoidal grating fringe images and at least one set of second sinusoidal grating fringe images, obtain the second three-dimensional point cloud data of the surface of the object being measured.

[0081] The aforementioned "another group" refers to another group besides "one group" in S120 among the two groups of first sinusoidal grating fringe images. The aforementioned "at least one group of second sinusoidal grating fringe images" refers to all groups of second sinusoidal grating fringe images, which is the same as the "at least one group of second sinusoidal grating fringe images" in S120.

[0082] In S130, at least one set of second sinusoidal grating fringe images is reused. Two sets of three-dimensional point cloud data are formed by using at least one set of second sinusoidal grating fringe images respectively through the two sets of sinusoidal grating fringe images, thereby expanding the amount of three-dimensional point cloud data.

[0083] The second three-dimensional point cloud data also incorporates the advantages of the first and second sinusoidal grating fringe images, thus the second three-dimensional point cloud data also has high accuracy.

[0084] S140: After converting the first three-dimensional point cloud data and the second three-dimensional point cloud data to the same pose, they are fused to obtain the third three-dimensional point cloud data of the surface of the object being measured.

[0085] 3D scanning equipment can be divided into handheld and fixed types. Handheld 3D scanning equipment can be further divided into two types: the object being measured remains stationary while the user holds the handheld 3D scanning equipment to scan the object; or the handheld 3D scanning equipment remains stationary while the object being measured rotates on a turntable. Regardless of the type of handheld 3D scanning equipment, its pose relative to the object being measured is constantly changing. Therefore, it is necessary to convert the two to the same pose and then fuse them to obtain the third-dimensional point cloud data of the object's surface.

[0086] If a fixed 3D scanning device is installed in a fixed position and there is no change in the relative position between the fixed 3D scanning device and the object being measured, there will be no pose difference between the first 3D point cloud data and the second 3D point cloud data. If there is a change in the relative position between the fixed 3D scanning device and the object being measured, there will be a pose difference between the first 3D point cloud data and the second 3D point cloud data, and the two need to be converted to the same pose before fusion.

[0087] S150: Determine the three-dimensional surface model of the object under test based on the third-dimensional point cloud data.

[0088] After obtaining the third-dimensional point cloud data, point cloud registration, surface reconstruction, and texture mapping (optional steps) are required to determine the three-dimensional surface model of the object being measured.

[0089] The three-dimensional reconstruction method of this application embodiment can be implemented based on a monocular camera, a binocular camera, or a multi-view camera, and this embodiment does not limit it.

[0090] This application embodiment designs two sets of first sinusoidal grating fringe images with a high period count (i.e., high frequency) and at least one set of second sinusoidal grating fringe images with a low period count (i.e., low frequency) within the coded fringe pattern. The at least one set of second sinusoidal grating fringe images with a low period count is reused, and two 3D point cloud data are reconstructed from the two sets of first sinusoidal grating fringe images respectively. The two 3D point cloud data are then converted to the same pose and fused. Thus, two 3D point cloud data are reconstructed from one coded fringe pattern. This approach has at least the following advantages:

[0091] (1) After the two 3D point cloud data are fused, there is more point cloud data, resulting in better 3D reconstruction effect.

[0092] (2) It can shorten the scanning time and improve the efficiency of three-dimensional reconstruction.

[0093] (3) The complementary advantages of sinusoidal grating fringe images with a high period count and a low period count are combined. The first sinusoidal grating fringe image with a high period count provides detail, while the second sinusoidal grating fringe image with a low period count provides global stability and robustness. This combination helps to reduce errors and improve overall accuracy during 3D reconstruction.

[0094] In an optional embodiment of this application, S120: obtaining the first three-dimensional point cloud data of the surface of the object under test based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images, specifically including the following steps:

[0095] S121: Determine the phase distribution of each pixel in a set of first sinusoidal grating fringe images based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images.

[0096] When a sinusoidal grating fringe image is projected onto the surface of the object being measured, the inconsistent height of the surface will cause the sinusoidal grating fringe image to be distorted or deformed on the surface. The phase of the sinusoidal grating fringe image changes in the distorted part. By measuring these distorted fringe patterns, the principal phase value of each pixel can be calculated, and the phase distribution can be obtained after phase unrolling.

[0097] S122: Based on the phase distribution of each pixel in a set of first sinusoidal grating fringe images, obtain the first depth information of the surface of the object being measured.

[0098] The distortion or deformation of the sinusoidal grating fringe image on the object being measured contains the height information of the object's surface, which in turn can determine the depth information between the object's surface and the 3D scanning device.

[0099] S123: Based on the first depth information of the surface of the object being measured, obtain the first three-dimensional point cloud data of the surface of the object being measured.

[0100] By combining the intrinsic and extrinsic parameters of the camera in the 3D scanning device, the depth of each pixel is converted into 3D point cloud data.

[0101] The first three-dimensional point cloud data in this embodiment is determined by the phase distribution of each pixel in the first sinusoidal grating fringe image. Since the first sinusoidal grating fringe image can carry more height information of the surface of the measured object, the reconstruction accuracy is higher.

[0102] In an optional embodiment of this application, the group of second sinusoidal grating fringe images with the fewest period numbers in at least one group of second sinusoidal grating fringe images has a period number of 1; each group of first sinusoidal grating fringe images and second sinusoidal grating fringe images has at least three sinusoidal grating fringe images, and the at least three sinusoidal grating fringe images have the same period number and a fixed phase difference. Specifically, S121: Based on a group of first sinusoidal grating fringe images and at least one group of second sinusoidal grating fringe images, determine the phase distribution of each pixel in a group of first sinusoidal grating fringe images, specifically including the following steps:

[0103] S1211: Solve the phase of a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images respectively, and obtain the phase principal value of the set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images at each pixel.

[0104] Phase determination refers to the distortion of an originally flat sinusoidal grating fringe image due to changes in the height of an object's surface. This distortion reflects the object's surface height information. By solving for these distorted sinusoidal grating fringe images, the principal phase value of each pixel can be obtained. Specifically, the principal phase value of each pixel can be determined using the phase-shifting method.

[0105] The principal phase value refers to the relative value of the phase angle of a pixel within its own period.

[0106] For example, at least one set of second sinusoidal grating fringe images includes three sets of sinusoidal grating fringe images with period numbers of 16, 4, and 1, and one set of first sinusoidal grating fringe images is a set of sinusoidal grating fringe images with a period number of 64. The period number of 1 ( Figure 2a Three sinusoidal grating fringe images are projected onto the surface of the object being measured. Each sinusoidal grating fringe image has the same amplitude (the degree of difference between bright and dark fringes) and a fixed phase difference of 0°, 120°, and 240° (i.e., 0, 2π / 3, and 4π / 3). The camera captures the light intensity changes on these sinusoidal grating fringe images, and the grayscale value of each pixel in each image is extracted. The principal phase value of each pixel is calculated using a three-step phase-shifting method. Since a sinusoidal grating fringe image with a period of 1 has only one period, the principal phase value is unique within that period. Therefore, the principal phase value of each pixel is uniquely determined, and the phase distribution of each pixel can be determined based on its principal phase value. Phase distribution refers to the distribution of phase angles with position or time across the entire projection area. Besides the three-step phase-shifting method, a sinusoidal grating fringe image with a period of 1 (… Figure 2a Four sinusoidal grating fringe images are projected onto the surface of the object being measured. Each sinusoidal grating fringe image has the same amplitude and a fixed phase difference of 0°, 90°, 180°, and 270° (i.e., 0, π / 2, π, and π³ / 2). The light intensity changes on these sinusoidal grating fringe images are captured by a camera, and the grayscale value of each pixel in the sinusoidal grating fringe images is extracted. The principal phase value of each pixel in the sinusoidal grating fringe images is calculated using a four-step phase-shifting method.

[0107] Similarly, the phase principal value of each pixel in a sinusoidal grating fringe image with a period number of 4 can be determined using a three-step or four-step phase-shifting method. However, since there are 4 periods in the entire projection area, the phase principal value of any pixel has 3 other phase principal values ​​equal to it. This is because the phase principal value is unique within one period, but there will be 4 identical phase principal values ​​across four periods. The phase principal value of each pixel in sinusoidal grating fringe images with a period number of 16 and 64 can be determined using a three-step or four-step phase-shifting method. A sinusoidal grating fringe image with a period number of 16 has 16 identical phase principal values, and a sinusoidal grating fringe image with a period number of 64 has 64 identical phase principal values.

[0108] S1212: Align the phase principal values ​​of a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images at the period edges.

[0109] For example, after obtaining the principal phase values ​​of sinusoidal grating fringe images with period numbers of 1, 4, 16, and 64, it is necessary to align the principal phase values ​​of these sinusoidal grating fringe images at the period edges to facilitate phase unwrapping. Period edge alignment means that the initial phases are all the same, for example, all are 0 phase; and the final phases are also the same, for example, all are 2π.

[0110] S1213: Based on the period number from smallest to largest, and according to the multiple relationship of the period number between two adjacent second sinusoidal grating fringe images, determine the phase distribution of the next second sinusoidal grating fringe image at each pixel according to the phase principal value of the previous second sinusoidal grating fringe image at each pixel.

[0111] The term "step by step" should be understood as "in order of increasing cycle number".

[0112] After obtaining the principal phase values ​​in S1211, these principal phase values ​​are enclosed in the interval (-π, π]. Therefore, they do not have full-field continuity and require phase unwrapping to obtain the phase distribution of each pixel. Phase unwrapping, also known as phase dewrapping, is the process of recovering the true phase value from the measurement results of multiple phase wrappers. When calculating the phase, due to the use of periodic functions such as the arctangent function, the obtained phase values ​​are often enclosed in a fixed phase interval (such as -π to π), which is called wrapped phase. The goal of phase unwrapping is to expand these wrapped phase values ​​into continuous true phase values, obtaining an unambiguous phase distribution across the entire field, thereby accurately reflecting the shape and depth information of the object's surface.

[0113] For example, arranging the four sets of sinusoidal grating fringe images in ascending order of period number means arranging them in the order of 1, 4, 16, and 64. The sinusoidal grating fringe images with a period number of 1 and a period number of 4 are adjacent, and their ratio is 4. The phase distribution of the next sinusoidal grating fringe image at each pixel is determined based on the principal phase value of the preceding second sinusoidal grating fringe image at each pixel. This is the phase unwrapping process.

[0114] The series k can be determined by multiplying the phase principal value of a pixel in a sinusoidal grating fringe image with period number 1 by 4 and subtracting the phase value of the corresponding pixel in a sinusoidal grating fringe image with period number 4 by 2π. The series k represents the period range within which the phase principal value falls: k = 0 means the phase principal value falls in the first period, k = 1 means the phase principal value falls in the second period, and so on, until k = 3 means the phase principal value falls in the fourth period.

[0115] For example, if the principal phase value of a pixel in a sinusoidal grating fringe image with a period number of 1 is π, and the principal phase value of the same pixel in a sinusoidal grating fringe image with a period number of 4 is 0, then the series k = (π*4-0) / 2π = 2, indicating that the pixel is in the third period of the sinusoidal grating fringe image with a period number of 4.

[0116] Since the period range of a sinusoidal grating fringe image with a period number of 4 is 0-8π (4 x 2π), and the series number of the pixel is known to be k=2, in order to determine the specific position of the pixel in the 0-8π range (i.e., phase distribution), it is necessary to take the principal phase value of the pixel in the sinusoidal grating fringe image with a period number of 4 and add k*2π, that is, 0+2*2π=4π.

[0117] In a sinusoidal grating fringe image with a period of 4, there are four pixels with a phase principal value of 0. Without phase unrolling, it is impossible to know where these four pixels with a phase principal value of 0 are located. Now, by phase unrolling, the specific position (i.e., phase distribution) of each pixel in the range of 0-8π can be determined.

[0118] Knowing the phase distribution of each pixel in a sinusoidal grating fringe image with a period of 4, and the principal phase value of each pixel in a sinusoidal grating fringe image with a period of 16, allows us to determine the phase distribution of each pixel in the sinusoidal grating fringe image with a period of 16. For example, a pixel might have a principal phase value of 4π in a sinusoidal grating fringe image with a period of 4, and a principal phase value of 0 in a sinusoidal grating fringe image with a period of 16. However, there are 16 pixels with a principal phase value of 0 in a sinusoidal grating fringe image with a period of 16. In this case, we need to determine the specific location of each pixel with a principal phase value of 0. Through phase expansion, we first determine the series k = (4π*4-0) / 2π = 8, indicating that the pixel is located in the ninth period of the sinusoidal grating fringe image with a period of 16. Since the period range of a sinusoidal grating fringe image with a period number of 16 is 0-32π (16 2π periods), and the order of the pixel is known to be k=8, to determine the specific position (i.e., phase distribution) of the pixel within the 0-32π range, we need to add k*2π to the principal phase value of the pixel in the sinusoidal grating fringe image with a period number of 16, i.e., 0+8*2π=16π. Similarly, the phase distribution of the remaining pixels can be determined.

[0119] S1214: Determine the phase distribution of each pixel in a set of first sinusoidal grating fringe images based on the phase distribution of each pixel in the second sinusoidal grating fringe image with the largest number of periods in at least one set of second sinusoidal grating fringe images, and the multiple relationship between the number of periods of the second sinusoidal grating fringe image with the largest number of periods in at least one set of second sinusoidal grating fringe images and a set of first sinusoidal grating fringe images.

[0120] For example, among sinusoidal grating fringe images with period numbers of 1, 4, and 16, the sinusoidal grating fringe image with a period number of 16 has the most periods. Based on the phase distribution of each pixel in the sinusoidal grating fringe image with a period number of 16 and the principal phase value of each pixel in the sinusoidal grating fringe image with a period number of 64, the phase distribution of each pixel in the sinusoidal grating fringe image with a period number of 64 can be determined. This completes the phase unwrapping of all pixels.

[0121] In an optional embodiment of this application, S130: Based on another set of first sinusoidal grating fringe images and at least one set of second sinusoidal grating fringe images, the second three-dimensional point cloud data of the surface of the object under test is obtained. The specific steps can be the same as the specific sub-steps of S120 (S121-S123 and S1211-S1214), so this embodiment will not repeat them.

[0122] Preferably, the 3D reconstruction method of this application embodiment is suitable for close-range scanning, that is, when the distance between the 3D scanning device and the object being measured is less than or equal to a set distance, thereby achieving better scanning results. During long-range scanning (when the distance between the 3D scanning device and the object being measured is greater than a set distance), the power of the projection device in the 3D scanning device is limited, resulting in unclear projected sinusoidal grating fringes, which affects the 3D reconstruction effect. To compensate for the impact of long-range scanning, this application embodiment can employ a speckle projection system for long-range 3D reconstruction.

[0123] In this embodiment, the phase distribution of each pixel in the remaining sets of second sinusoidal grating fringe images is progressively phase-expanded using the principal phase value of the second sinusoidal grating fringe image with a period of 1. Then, the phase distribution of each pixel in a set of first sinusoidal grating fringe images is expanded based on the phase distribution of each pixel in the second sinusoidal grating fringe image with the largest period. In other words, since low-frequency sinusoidal grating fringe images have small errors during phase expansion, at least one set of second sinusoidal grating fringe images (i.e., low-frequency sinusoidal grating fringe images) in this embodiment is mainly for phase expansion, thereby correcting the errors caused by factors such as phase deconvolution and environmental interference in the first sinusoidal grating fringe image (i.e., high-frequency sinusoidal fringe image). This results in the first sinusoidal grating fringe image having a high-accuracy phase distribution. Combined with the inherent high precision of the first sinusoidal grating fringe image, the reconstruction accuracy is high.

[0124] In an optional embodiment of this application, the three-dimensional reconstruction method further includes:

[0125] S160: Determine the moving speed of the 3D scanning device based on the pose of the first 3D point cloud data and the pose of the second 3D point cloud data.

[0126] S170: Save and / or display in real time the moving speed of the 3D scanning device.

[0127] Since the 3D reconstruction method of this application embodiment can be applied to handheld 3D scanning devices, and the scanning speed of the handheld 3D scanning device cannot exceed a preset speed, otherwise it will significantly affect the 3D reconstruction effect. However, users cannot scan at a constant speed like machines, so there is a possibility that the moving speed may exceed the preset speed. Based on this, this application embodiment determines the moving speed of the 3D scanning device based on the pose of the first 3D point cloud data and the pose of the second 3D point cloud data, and saves the moving speed of the 3D scanning device, so that in the case of exceeding the speed limit, the exceeding part can be scanned again. Alternatively, the moving speed of the 3D scanning device can be displayed, and when the user finds that it is close to the preset speed, the moving speed can be reduced.

[0128] In an optional embodiment of this application, the three-dimensional reconstruction method further includes:

[0129] S180: Issue a warning message when the moving speed of the 3D scanning device exceeds the preset speed.

[0130] To further alert users to reduce scanning speed when they are about to exceed the speed limit or have already exceeded it, a warning message can be issued when the movement speed exceeds a preset speed. The preset speed can be equal to the set maximum movement speed or equal to 90% of the set maximum movement speed. The warning message can be an audible alert, a flashing indicator light, a screen flash, or a warning text message.

[0131] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0132] Figure 3 A partial structural schematic diagram of a three-dimensional scanning device disclosed in an embodiment of this application is shown. The three-dimensional scanning device includes a projection device 310, a camera 320, and a processor.

[0133] Projection device 310 is used to project a time-varying coded stripe pattern onto the surface of an object being measured; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images.

[0134] Camera 320 is used to capture the coded stripe pattern on the surface of the object being tested.

[0135] The processor is configured to: obtain first three-dimensional point cloud data of the surface of a measured object based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images; obtain second three-dimensional point cloud data of the surface of the measured object based on another set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images; fuse the first three-dimensional point cloud data and the second three-dimensional point cloud data after converting them to the same pose to obtain third three-dimensional point cloud data of the surface of the measured object; and determine a three-dimensional surface model of the measured object based on the third three-dimensional point cloud data.

[0136] For example, the projection device is a MEMS structured light module 311 or a DLP (Digital Light Processing Projector) optical engine. The MEMS structured light module 311 is preferred because it is smaller and cheaper, enabling miniaturization of the 3D scanning device.

[0137] Furthermore, the projection device 310 in this embodiment also includes a speckle projector 312. At close range, the MEMS structured light module 311 projects a time-varying coded stripe pattern onto the surface of the object under test for three-dimensional reconstruction. At long range, the speckle projector 312 projects speckles onto the surface of the object under test for three-dimensional reconstruction.

[0138] Figure 4 A partial structural schematic diagram of another three-dimensional scanning device disclosed in an embodiment of this application is shown. Furthermore, the projection device 310 of this application embodiment also includes a single-line or multi-line laser module 313, which can be selected as needed for close-range scanning, or a single-line or multi-line laser module 313 or a MEMS structured light module 311.

[0139] It should be noted that the camera of the 3D scanning device in this application embodiment can be a monocular camera, a binocular camera, or a multi-view camera.

[0140] The 3D scanning device disclosed in this embodiment has a processor, so calculation processes such as phase deconstruction, phase unwinding, and 3D model construction can all be completed within the 3D scanning device. Furthermore, the 3D scanning device has a built-in screen to display the 3D model, making the 3D scanning device more convenient.

[0141] Figure 5 A schematic diagram of the structure of a three-dimensional scanning system disclosed in an embodiment of this application is shown, including a three-dimensional scanning device 510 and an electronic device 520.

[0142] The three-dimensional scanning device 510 is used to project a time-varying coded stripe pattern onto the surface of the object being measured; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images.

[0143] The 3D scanning device 510 is also used to capture the coded stripe pattern on the surface of the object being measured.

[0144] Electronic device 520 is configured to obtain first three-dimensional point cloud data of the surface of a measured object based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images; obtain second three-dimensional point cloud data of the surface of the measured object based on another set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images; fuse the first three-dimensional point cloud data and the second three-dimensional point cloud data after converting them to the same pose to obtain third three-dimensional point cloud data of the surface of the measured object; and determine a three-dimensional surface model of the measured object based on the third three-dimensional point cloud data.

[0145] The electronic device 520 in this embodiment includes, but is not limited to, devices with high computing power such as computers, mobile phones, and servers.

[0146] In this embodiment of the 3D scanning system, the 3D scanning device 510 is mainly used for projecting and photographing coded stripe patterns. Data is transmitted between the 3D scanning device 510 and the electronic device 520 via a wired or wireless connection. The calculation processes, including phase deconvolution, phase unrolling, and 3D model construction, are all performed by the electronic device 520.

[0147] Corresponding to the three-dimensional reconstruction method described in the above embodiments, Figure 6 A structural block diagram of the three-dimensional reconstruction device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0148] Reference Figure 6 The device includes:

[0149] The acquisition module 610 is used to acquire a time-varying coded stripe pattern projected by the 3D scanning device onto the surface of the object under test; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images;

[0150] The first determining module 620 is used to obtain the first three-dimensional point cloud data of the surface of the object under test based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images.

[0151] The second determining module 630 is used to obtain the second three-dimensional point cloud data of the surface of the object under test based on another set of first sinusoidal grating fringe images and at least one set of second sinusoidal grating fringe images.

[0152] The fusion module 640 is used to convert the first three-dimensional point cloud data and the second three-dimensional point cloud data to the same pose and then fuse them to obtain the third three-dimensional point cloud data of the surface of the object under test.

[0153] The third determination module 650 is used to determine the three-dimensional surface model of the object under test based on the third three-dimensional point cloud data.

[0154] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0155] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0156] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0157] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0158] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a 3D scanning device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0159] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0160] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages ​​such as Python, Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0161] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0162] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0163] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices / 3D scanning devices and methods can be implemented in other ways. For example, the device / electronic device / 3D scanning device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A three-dimensional reconstruction method, characterized in that, include: Acquire a time-varying coded stripe pattern projected onto the surface of a measured object by a 3D scanning device; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images; Based on a set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images, the first three-dimensional point cloud data of the surface of the object under test is obtained; Based on another set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images, the second three-dimensional point cloud data of the surface of the object under test is obtained; The first three-dimensional point cloud data and the second three-dimensional point cloud data are converted to the same pose and then fused to obtain the third three-dimensional point cloud data of the surface of the object under test. Based on the third three-dimensional point cloud data, the three-dimensional surface model of the object under test is determined.

2. The three-dimensional reconstruction method as described in claim 1, characterized in that, The step of obtaining the first three-dimensional point cloud data of the surface of the object under test based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images includes: Based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images, determine the phase distribution of each pixel in a set of first sinusoidal grating fringe images; Based on the phase distribution of each pixel in a set of the first sinusoidal grating stripe images, the first depth information of the surface of the object under test is obtained; Based on the first depth information of the surface of the object being measured, the first three-dimensional point cloud data of the surface of the object being measured is determined.

3. The three-dimensional reconstruction method as described in claim 2, characterized in that, The group of second sinusoidal grating fringe images with the fewest period numbers in the at least one group of second sinusoidal grating fringe images has a period number of 1; each group of first sinusoidal grating fringe images and second sinusoidal grating fringe images has at least three sinusoidal grating fringe images, and the at least three sinusoidal grating fringe images have the same period number and a fixed phase difference; The step of determining the phase distribution of each pixel in a set of first sinusoidal grating fringe images based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images includes: The phase of a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images are solved respectively to obtain the phase principal value of a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images at each pixel. Align the phase principal values ​​of a set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images at the period edges; Taking the period number as the order of small to large, and based on the multiple relationship of the period number between two adjacent second sinusoidal grating stripe images, the phase distribution of the next second sinusoidal grating stripe image at each pixel is determined step by step according to the phase principal value of the previous second sinusoidal grating stripe image at each pixel. The phase distribution of each pixel in a set of first sinusoidal grating fringe images is determined based on the phase distribution of each pixel in the second sinusoidal grating fringe image with the largest number of periods in the at least one set of second sinusoidal grating fringe images, and the multiple relationship between the number of periods of the second sinusoidal grating fringe image with the largest number of periods in the at least one set of second sinusoidal grating fringe images and a set of first sinusoidal grating fringe images.

4. The three-dimensional reconstruction method according to any one of claims 1-3, characterized in that, The method further includes: The moving speed of the 3D scanning device is determined based on the pose of the first 3D point cloud data and the pose of the second 3D point cloud data. Save and / or display in real time the moving speed of the 3D scanning device.

5. The three-dimensional reconstruction method as described in claim 4, characterized in that, The method further includes: When the moving speed of the 3D scanning device exceeds the preset speed, a warning message is issued.

6. A three-dimensional scanning device, comprising a projection device, a camera, and a processor, characterized in that, The projection device is used to project a time-varying coded stripe pattern onto the surface of the object being measured; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images. The camera is used to capture the coded stripe pattern on the surface of the object being measured; The processor is configured to obtain first three-dimensional point cloud data of the surface of the object under test based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images. Based on another set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images, the second three-dimensional point cloud data of the surface of the object under test is obtained; The first three-dimensional point cloud data and the second three-dimensional point cloud data are converted to the same pose and then fused to obtain the third three-dimensional point cloud data of the surface of the object under test. as well as Based on the third three-dimensional point cloud data, the three-dimensional surface model of the object under test is determined.

7. The three-dimensional scanning device as described in claim 6, characterized in that, The projection device is a MEMS structured light module.

8. A three-dimensional scanning system, comprising a three-dimensional scanning device and an electronic device, characterized in that, The three-dimensional scanning device is used to project a time-varying coded stripe pattern onto the surface of the object being measured; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images. The three-dimensional scanning device is also used to capture the coded stripe pattern on the surface of the object being measured; The electronic device is used to obtain first three-dimensional point cloud data of the surface of the object under test based on a set of first sinusoidal grating fringe images and at least a set of second sinusoidal grating fringe images. Based on another set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images, the second three-dimensional point cloud data of the surface of the object under test is obtained; The first three-dimensional point cloud data and the second three-dimensional point cloud data are converted to the same pose and then fused to obtain the third three-dimensional point cloud data of the surface of the object under test. as well as Based on the third three-dimensional point cloud data, the three-dimensional surface model of the object under test is determined.

9. A three-dimensional reconstruction device, characterized in that, include: The acquisition module is used to acquire a time-varying coded stripe pattern projected by a 3D scanning device onto the surface of the object under test; the coded stripe pattern includes two sets of first sinusoidal grating stripe images with the same number of periods and at least one set of second sinusoidal grating stripe images with fewer periods than the first sinusoidal grating stripe images; The first determining module is used to obtain the first three-dimensional point cloud data of the surface of the object under test based on a set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images. The second determining module is used to obtain the second three-dimensional point cloud data of the surface of the object under test based on another set of the first sinusoidal grating fringe images and the at least one set of the second sinusoidal grating fringe images. The fusion module is used to convert the first three-dimensional point cloud data and the second three-dimensional point cloud data to the same pose and then fuse them to obtain the third three-dimensional point cloud data of the surface of the object under test. The third determining module is used to determine the three-dimensional surface model of the object under test based on the third three-dimensional point cloud data.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

Images